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        <h1 id="第3课-机器学习构建chatbot"><a href="#第3课-机器学习构建chatbot" class="headerlink" title="第3课 机器学习构建chatbot"></a>第3课 机器学习构建chatbot</h1><h2 id="主要内容"><a href="#主要内容" class="headerlink" title="主要内容"></a>主要内容</h2><h3 id="关于聊天机器人的思考"><a href="#关于聊天机器人的思考" class="headerlink" title="关于聊天机器人的思考"></a>关于聊天机器人的思考</h3><h4 id="1-工程考量"><a href="#1-工程考量" class="headerlink" title="1.工程考量"></a>1.工程考量</h4><h4 id="2-机器学习角度考虑"><a href="#2-机器学习角度考虑" class="headerlink" title="2.机器学习角度考虑"></a>2.机器学习角度考虑</h4><h3 id="预备知识"><a href="#预备知识" class="headerlink" title="预备知识"></a>预备知识</h3><h4 id="1-检索与匹配"><a href="#1-检索与匹配" class="headerlink" title="1.检索与匹配"></a>1.检索与匹配</h4><h4 id="2-分类与朴素贝叶斯"><a href="#2-分类与朴素贝叶斯" class="headerlink" title="2.分类与朴素贝叶斯"></a>2.分类与朴素贝叶斯</h4><h3 id="chatterbot"><a href="#chatterbot" class="headerlink" title="chatterbot"></a>chatterbot</h3><h4 id="1-架构与使用方法"><a href="#1-架构与使用方法" class="headerlink" title="1.架构与使用方法"></a>1.架构与使用方法</h4><h4 id="2-源码分析"><a href="#2-源码分析" class="headerlink" title="2.源码分析"></a>2.源码分析</h4><h2 id="传统聊天机器人"><a href="#传统聊天机器人" class="headerlink" title="传统聊天机器人"></a>传统聊天机器人</h2><h3 id="NLP基础知识"><a href="#NLP基础知识" class="headerlink" title="NLP基础知识"></a>NLP基础知识</h3><h4 id="基本分词"><a href="#基本分词" class="headerlink" title="基本分词"></a>基本分词</h4><h4 id="关键词抽取-tf-idf等"><a href="#关键词抽取-tf-idf等" class="headerlink" title="关键词抽取(tf-idf等)"></a>关键词抽取(tf-idf等)</h4><h4 id="正则表达式模式匹配"><a href="#正则表达式模式匹配" class="headerlink" title="正则表达式模式匹配"></a>正则表达式模式匹配</h4><h3 id="Machine-Learning相关知识"><a href="#Machine-Learning相关知识" class="headerlink" title="Machine Learning相关知识"></a>Machine Learning相关知识</h3><h4 id="文本表示与匹配"><a href="#文本表示与匹配" class="headerlink" title="文本表示与匹配"></a>文本表示与匹配</h4><h4 id="分类-文本场景分析"><a href="#分类-文本场景分析" class="headerlink" title="分类(文本场景分析)"></a>分类(文本场景分析)</h4><h4 id="数据驱动-特征工程"><a href="#数据驱动-特征工程" class="headerlink" title="数据驱动(特征工程)"></a>数据驱动(特征工程)</h4><h2 id="聊天机器人的一些思考"><a href="#聊天机器人的一些思考" class="headerlink" title="聊天机器人的一些思考"></a>聊天机器人的一些思考</h2><h3 id="工程考量"><a href="#工程考量" class="headerlink" title="工程考量"></a>工程考量</h3><h4 id="架构设计清晰、模块化"><a href="#架构设计清晰、模块化" class="headerlink" title="架构设计清晰、模块化"></a>架构设计清晰、模块化</h4><h4 id="功能分拆，解耦，部件可插拔与扩展"><a href="#功能分拆，解耦，部件可插拔与扩展" class="headerlink" title="功能分拆，解耦，部件可插拔与扩展"></a>功能分拆，解耦，部件可插拔与扩展</h4><p><img src="image-20220130190100167.png" alt="image-20220130190100167"></p>
<h2 id="聊天机器人的一些思考-1"><a href="#聊天机器人的一些思考-1" class="headerlink" title="聊天机器人的一些思考"></a>聊天机器人的一些思考</h2><h3 id="算法与机器学习角度考量"><a href="#算法与机器学习角度考量" class="headerlink" title="算法与机器学习角度考量"></a>算法与机器学习角度考量</h3><h4 id="算法简单，数据-特征-驱动"><a href="#算法简单，数据-特征-驱动" class="headerlink" title="算法简单，数据(特征)驱动"></a>算法简单，数据(特征)驱动</h4><h4 id="场景化与垂直领域"><a href="#场景化与垂直领域" class="headerlink" title="场景化与垂直领域"></a>场景化与垂直领域</h4><h2 id="预备知识：检索与匹配"><a href="#预备知识：检索与匹配" class="headerlink" title="预备知识：检索与匹配"></a>预备知识：检索与匹配</h2><h3 id="基于检索与匹配"><a href="#基于检索与匹配" class="headerlink" title="基于检索与匹配"></a>基于检索与匹配</h3><h4 id="知识库（存储了问题与回复内容）"><a href="#知识库（存储了问题与回复内容）" class="headerlink" title="知识库（存储了问题与回复内容）"></a>知识库（存储了问题与回复内容）</h4><h4 id="检索：搜寻相关问题"><a href="#检索：搜寻相关问题" class="headerlink" title="检索：搜寻相关问题"></a>检索：搜寻相关问题</h4><h4 id="匹配：对结果进行排序"><a href="#匹配：对结果进行排序" class="headerlink" title="匹配：对结果进行排序"></a>匹配：对结果进行排序</h4><h2 id="预备知识：编辑距离"><a href="#预备知识：编辑距离" class="headerlink" title="预备知识：编辑距离"></a>预备知识：编辑距离</h2><h3 id="编辑距离"><a href="#编辑距离" class="headerlink" title="编辑距离"></a>编辑距离</h3><h4 id="编辑距离-Levenshtein距离，是指两个字符串之间，由一个转成另一个所需要的最少编辑操作次数。-u-允许的编辑操作包括："><a href="#编辑距离-Levenshtein距离，是指两个字符串之间，由一个转成另一个所需要的最少编辑操作次数。-u-允许的编辑操作包括：" class="headerlink" title="编辑距离/Levenshtein距离，是指两个字符串之间，由一个转成另一个所需要的最少编辑操作次数。 u 允许的编辑操作包括："></a>编辑距离/Levenshtein距离，是指两个字符串之间，由一个转成另一个所需要的最少编辑操作次数。 u 允许的编辑操作包括：</h4><ul>
<li>将一个字符替换成另一个字符</li>
<li>插入一个字符</li>
<li>删除一个字符<br><img src="image-20220130191617429.png" alt="image-20220130191617429"><br><img src="image-20220130191622211.png" alt="image-20220130191622211"><br>预备知识：python编辑距离<br>Python在string 类型中，默认的utf-8 编码下，一个中文字符是用三个字节来表示的。用unicode。<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"># -*- coding:utf-8 -*- </span><br><span class="line">import Levenshtein </span><br><span class="line">texta = &#x27;爱动脑的小王欣&#x27; </span><br><span class="line">textb = &#x27;爱动脑的小王欣&#x27; </span><br><span class="line">print(Levenshtein.distance(texta,textb))</span><br></pre></td></tr></table></figure>
预备知识：场景分类与NB<script type="math/tex; mode=display">
P(B|A)=\frac{P(A|B)P(B)}{P(A)}</script></li>
</ul>
<p><img src="1.png" alt="1"></p>
<p>o 每个部分都设计了不同的“适配器”(Adapter) </p>
<p>o机器人应答逻辑 =&gt; Logic Adapters </p>
<p>oClosest Match Adapter </p>
<p>l 字符串模糊匹配(编辑距离) </p>
<p>oClosest Meaning Adapter </p>
<p>l 借助nltk的WordNet，近义词评估 </p>
<p>oTime Logic Adapter </p>
<p>l 处理涉及时间的提问 </p>
<p>oMathematical Evaluation Adapter </p>
<p>l 涉及数学运算<br>每个部分都设计了不同的“适配器”(Adapter)<br>o存储器后端 =&gt; Storage Adapters<br>oRead Only Mode<br>l 只读模式，当有输入数据到chatterbot的时候，数<br>据库并不会发生改变<br>oJson Database Adapter<br>l 用以存储对话数据的接口，对话数据以Json格式<br>进行存储。 oMongo Database Adapter<br>l 以MongoDB database方式来存储对话数据</p>

      
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